Sensor Web Approach for Earthquake studies. D. Ouzounov

1 Application of Remote Sensing Technologies for Disaster Risk Management: Mutisensor approach of analyzing atmospheric signals and search for possible earthquake precursors D. Ouzounov 1,4, S. Habib 2, and S. Ambrose 3 1 NASA Goddard Space Flight Center/SSAI, MS 610 Greenbelt, MD 20177, United States 2 NASA Goddard Space Flight Center, Science & Exploration Directorate, MS 604, MD 20177, United States 3 NASA Headquarters, Applied Sciences Program, Disaster Management Program, Washington, DC 20546, United States 4 Chapman University, United States NASA - P.Taylor, F.Policelli, N. Bryant, NASA JPL/Caltech GMU -G.Cervone, M.Kafatos S. Pulinets, IZMIRAN, Russia M.Parrot, DEMETER,CNES, France S.Uyeda,K. Hattori, Chiba University, Japan V.Tramutoli, University of Basilicata, Italy G.Ciarlo, CNRS, Florence, Italy X. Shen, Chen I-wan,China Earthquake Administration, China J.Y.Liu, H-C. Wu, National central University, Taiwan Turkey, Greece, Israel Dimitar.P.Ouzounov@nasa.gov

Outline Background Early earthquake warning systems LAIC model and validation Sensor Web Approach Validation Hind casting and real alerts May 12th, M7.8 Sichuan earthquake, China Summary

Overview Multisensor approach for tracking earthquake atmospheric signals This study is on early stage of validation. The purpose of this study is to utilize global remote-sensing satellite data (thermal infrared observations from Terra, Aqua, GOES, POES, METEOSAT, space plasma parameters from DEMETER,DMSP and COSMIC), simultaneously with ground observations to detect and understand atmospherics signals prior to major earthquakes. Our approach is integrated analysis (Sensor Web) of satellite and ground measurements. Our main goal to establish a historical multi-year (10-20 years) time series baseline by continuous monitoring of the multi atmospheric and ionospheric signals (precursors) over different seismo-tectonic background.

The Grand Challenges for Disaster Reduction http://www.sdr.gov 1. 2. 3. 4. 5. 6. Provide hazard and disaster information where and when it is needed. Understand the natural processes that produce hazards. Develop hazard mitigation strategies and technologies. Recognize and reduce vulnerability of interdependent critical infrastructure. Assess disaster resilience using standard methods. Promote risk-wise behavior.

Implementing the Grand Challenges Fully explore the predictability of earthquakes based on testable and credible methods, and provide objective reviews of predictions.. http://www.sdr.gov

Do we have a choice? Earthquake/Tsunami Occurrence of Floods Contaminated Fresh Water Supply Spread of Multiple Infectious Diseases Volcanic Eruption Aerosol and Dust deposition and suspension Agriculture Efficiency Breathing problems Severe Weather Fires Air Quality Public Health Electric Grid Outages Shutdown City Anthropogenic or Technological

Outline Background Early earthquake warning systems LAIC model and validation Sensor Web Approach Validation Hind casting and real alerts May 12th, M7.8 Sichuan earthquake, China Summary

Source of the Natural Hazards and Early Warning Systems Planning Avalanche Flood Recovery Disaster Management Cycle Mitigation Drought Landslide, Mudflow Forest Fire Volcano Response Severe Storm, Hurricane, Tornado Tsunami Earthquakes?

Map of earthquake early-warning systems Map showing the locations of earthquake early-warning systems currently in operation (blue) or development (green) around the world. Operational systems include Japan, Taiwan, Mexico, and Turkey. Systems are in development for California, Egypt, Greece, Iceland, Italy, Romania, and Switzerland. The locations are overlaid on the GSHAP global seismichazard map (Giardini et al., 1999). (After Allen, 2007)

NASA Applied Science Architecture for Integrated Solutions

Outline Background Early earthquake warning systems LAIC model and validation Sensor Web Approach Validation Hind casting and real alerts May 12th, M7.8 Sichuan earthquake, China Summary

Pre-earthquake related signals Planetary positions Ground deformations Geomagnetic methods Energy accumulation rate Earthquake clouds Gravity anomalies Ground water level Radon concentrations Meteorological conditions Thermal infrared Infrasound Crustal stress Abnormal behaviour of animals Geo-electric pulse Historical/statistical data Ground-based EM field Tilt meters GPS TGFR MS-Double Time Method Geo-electricity Micro-vibration Earth resistivity Geochemistry Seismic gap Foreshocks Geodesy Micro-changes Ionosphere Vandergeden(2005)

Mechanism of Lithosphere -Atmosphere Ionosphere Coupling (LAIC) Model L-A A-I OLR anomalies Air temperature growth Earthquake clouds formation Electric field effects within the ionosphere Latent heat release Convective ions uplift, charge separation, drift in anomalous EF Atmospheric electric field growth Humidity drop Ions hydration formation of aerosol size particles Air conductivity change Air ionization by α-particles product of radon decay Faults activation permeability changes Gas discharges including radon emanation (Pulinets, 2004, Pulinets, Ouzounov et al, 2006)

Alternative models 1. Tectonic heat flow 2. Thermal waters (Gorny,1998) 3. Greenhouse gases (Tronin,2000) 4. Positive holes (Freund,2004) 5. LAIC- Ionization by radon as one of the degassing components (Pulinets,2004 Ouzounov,2006)

The annual mean global energy balance of the Earthatmosphere system [Hobbs, 2000]

What anomaly means? TIR Signal T a ( r,t) = T ref ( r,t' )("/+)# $T ( r) V ref (r) ±ns(r) Time or distance [after Tramutoli, 2004]

Outline Background Early earthquake warning systems LAIC model and validation Sensor Web Approach Validation Hind casting and real alerts May 12th, M7.8 Sichuan earthquake, China Summary

Traditional Use of Sensors Observation->detection->modeling->monitoring-> forecasting?? Satellite Sensor Application Satellite Sensor Satellite Sensor Ground Sensor Ground Sensor Application

A Sensor Web Approach Observation->detection->modeling->monitoring-> forecasting Satellite Sensor Satellite Sensor Satellite Sensor Satellite Sensor Application Numerical Models Data Mining Virtual Sensor Application Application Application Ground Sensor Ground Sensor Ground Sensor Ground Sensor

What is a Sensor Web? SeWeb a coordinated observation infrastructure employing multiple sensors that are distributed on one or more platforms (Sherwood&Chein,2007) Sensors fusion -Space, Ground, Virtual Needed when Dedicated sensors do not exist Insufficient temporal and spatial resolution 1/ use of multiple and already validated physical measurements to be fused into one framework with the latest theoretical models; 2/ enabling model interactions with sensor webs; 3/ provided a feedback on data gaps that may then be acquired from other sources; 4/ provide advanced data mining algorithms to classify, cluster, and find patterns in the data. Sensormag.com

Outline Background Early earthquake warning systems LAIC model and validation Sensor Web Approach Validation Hind casting and real alerts May 12th, M7.8 Sichuan earthquake, China Summary

Validation Man-made effects - Chernobyl Methodology validation: Case of Chernobyl nuclear accident Russia, April 1986 a: April, 1986 AVHRR/OLR; b: May 1986

Outline Background Early earthquake warning systems LAIC model and validation Sensor Web Approach Validation Hind casting and real alerts May 12th, M7.8 Sichuan earthquake, China Summary

Case studies of TIR- earthquake analysis (hind casting mode) 150 M>5.0 (TIR,OLR,GPS/TEC, OLR, SLHF,T/H,DEMETER) & 4,000 M>5.0 earthquakes (SLHF) Global analysis of OLR variability prior to major earthquakes 2001-2005 Name Date M, H,(km) EQ mechanism OLRAnomaly W/m -2 Detection time,days Toll Bhuj, Gujarat India 01/26/2001 7.9 23.6 Thrust Fault 12-6 20,000 Southeastern Iran 12/26/2003 6.6 15.0 Strike-slip fault 8-5 31,000 Kashmir Pakistan 10/15/2005 7.6 10.0 Strike 12-6 100,000 Andama.Sumatra, 12/26/2004 9.0 28.6 Mega thrust 18-7 284,000

Case studies of earthquake analysis Up to today were analyzed more then 150 M>5.0 (TIR,OLR,GPS/TEC, OLR, SLHF,T/H,DEMETER) Bhuj, India 01/26/2001, M7.9 Anomaly detection Jan, 1 2001 Southeastern Iran 12/26/2003, M6.6 Anomaly detection Dec, 1 2003 Kashmir, Pakistan 10/15/2005 M7.6 Anomaly detection Oct, 1 2005 Andaman Isl. Sumatra 12/26/2004 M9.0 Anomaly detection Dec, 1 2004 Ouzounov et al, 2007

Northern Sumatra Dec 26,2004, M9.0 A/ Map of OLR monthly variations for November 2004, month prior to M9.0 Sumatra Andaman Island, Northern Sumatra of December 26, 2004. Epicenter (3.09N/94.26E) OLR anomaly [W/m 2 ] 80 40 0-40 -80 Sumatra, Oct-Dec 2004 2004 NOAA-16 OLR 2001-2004 OLR +1 SIGMA mean field M9.0 Andaman Island Northern Sumatra, 12/26/2004 10/1 10/6 10/11 10/16 10/21 10/26 10/31 11/5 11/10 11/15 11/20 11/25 11/30 12/5 12/10 12/15 12/20 12/25 12/30 Time, October-December 2004, [days] B/Time-series of daily OLR anomaly for October 1, 2004 December 31, 2004 over the epicenter of (3.09N/ 94.26E)

M6.6 Southeastern Iran, Dec 25 2003 Map of OLR monthly variations for November 2003 OLR anommaly [W/m -2 ] 40 20 0-20 -40-60 Iran, 2003 2001-2003 OLR +1 sigma mean 2003 NOAA-15 OLR M6.6 Southeastern Iran of 12/26/2003 10/1/03 10/11/03 10/21/03 10/31/03 11/10/03 11/20/03 11/30/03 12/10/03 12/20/03 12/30/03 1/9/04 Oct 1,2003 - Dec, 31 2003 Time-series of daily OLR anomaly for October 1, 2003 December 31, 2003 over the epicenter of (29.1N/ 58.2E)

Validation over Japan (forecast mode) Evolution of daily Earth radiation anomalies. Earthquake has occurred 2007-07-16 01:13 (Mw 6.7) NEAR WEST COAST OF HONSHU, JAPAN 37.6 138.4 July 4, 2007 Time evolution: July 4 EQ Alert July 14- EQ Warning July 16- EQ Event July 14, 2007 July 16, 2007, USGS

Outline Background Early earthquake warning systems LAIC model and validation Sensor Web Approach Validation Hind casting and real alerts May 12th, M7.8 Sichuan earthquake, China Summary

Anomaly maps of daily night-time TIR earth outgoing radiation over epicenter of M7.8 Eastern Sichuan, China May 3- May 14, 2008, 03 May 04 May 05 May 06 May 07 May 09 May 10 May 11 May 12 May 13 May 08 May 14 May [Ouzounov et al, 2008]

Time-series of daily night-time OLR variability for April 25 May 25, 2008 over the epicentral area M7.8 Eastern Sichuan [Ouzounov et al, 2008]

Summary Using the fundamental principles of atmospheric physics the LAIC model was updated in order to explain the most of the observed atmospheric thermal variations observed before the earthquakes Sensor Web approach of using different satellite sensors and different geophysical fields strongly support the model estimates Practical applications could be build for automatic identification of the earthquake precursors Why we need partnership and International cooperation? The complex and dynamic nature of the earthquake hazard risk on global scale requires spatial, spectral, and temporal coverage that is far beyond any single satellite mission.

Now, you might be interested to read more about: 1. Ouzounov D., S. Habib and S. 2008,Ambrose A Multisensor approach analyzing atmospheric signals for possible earthquake precursors. Application of Remote Sensing for Risk Management, In the book Risk Wise, International Disaster and Risk Conference (IDRC) Davos, Switzerland, Tudor Rose, pp.162-165 2. Ouzounov D., S. Habib, F. Policelli, P. Taylor, 2007, Learning new methodologies to deal with large disasters: Near space monitoring of thermal signals associated with large earthquakes. In the book Elements of Life, World Meteorological Organization, 124-130 3. Ouzounov D., D. Liu, C. Kang, G.Cervone, M. Kafatos, P. Taylor, 2007. Outgoing Long Wave Radiation Variability from IR Satellite Data Prior to Major Earthquakes, Tectonophysics, Volume 431, Issues 1-4, 20 February, pp. 211-220 4. Parrot M. and D.Ouzounov, 2006.Surveying the Earth's Electromagnetic Environment From Space, EOS, Transactions of American Geophysical Union,26 December,Vol.87, 52, pp.595 5. Pulinets S., D. Ouzounov, A. Karelin, K. Boyarchuk, L. Pokhmelnykh, 2006. The Physical Nature of Thermal Anomalies Observed Before Strong Earthquakes, Physics and Chemistry of the Earth, 31, 143-153 6. Pulinets S., D. Ouzounov L. Ciraolo, R. Singh, G. Cervone, A. Leyva, M.Dunajecka, Karelin, K. Boyarchuk, 2006. Thermal, Atmospheric and Ionospheric Anomalies Around the time of Colima M7.8 Earthquake of January 21, 2003, Annales Geophysicale, 24, 835-849 7. Ouzounov D., N. Bryant, T. Logan, S. Pulinets, P.Taylor, 2006. Satellite thermal IR phenomena associated with some of the major earthquakes in 1999-2004, Physics and Chemistry of the Earth, 31,154-163

Thank you Questions? Send email to Dimitar.P.Ouzounov@nasa.gov